Completed collaboration with the MicroArray Quality Control (MAQC) consortium whereby RNA-Seq data and microarray gene expression data were evaluated for concordance within a comprehensive study containing 27 chemicals representing multiple modes of action (MOAs). The findings will help bioinformaticians better analyze and compare gene expression data from RNA-seq and microarray platforms. Completed the phase-1 collaboration with the NIEHS mouse methylome workgroup whereby analytical approaches were utilized to investigate allele specific expression, differential methylation patterns and differential gene expression. The results will provide a better understanding of the baseline methylome in two NTP mice strains, one of which has a high incidence of spontaneous liver tumors. -------------------------------------------------------------------------------------------------- Continued the collaborative support of investigators' research: 1) We employed bioinformatics strategies to analyze genomic data. 2) We used our custom Extracting Patterns and Identifying co-expressed Genes (EPIG) analysis tool to find genes which respond differently to the order of chemotherapeutic drug administered to rats and to identify microRNAs differentially expressed between tissues. 3) We used statistical modeling of gene expression data from humans exposed to acetaminophen in order to identify early indicators of hepatotoxicity. -------------------------------------------------------------------------------------------------- Initiated the development of a method to extend the Extracting Patterns and Identifying co-expressed Genes (EPIG) tool in support of count data from RNA-seq. Also initiated the development of analytical methodologies to extract biological themes from clustering of gene expression data and enrichment of biological processes or mechanistic pathways. --------------------------------------------------------------------------------------------------